WEBVTT 00:00:00.000 --> 00:00:01.700 If you've ever applied for, well, just about 00:00:01.700 --> 00:00:03.980 anything that touches the financial world, a 00:00:03.980 --> 00:00:07.059 home loan, a new car, a credit card, or even 00:00:07.059 --> 00:00:09.240 just an apartment lease, you definitely know 00:00:09.240 --> 00:00:11.199 the feeling. There's this one moment of truth 00:00:11.199 --> 00:00:13.259 that usually lasts only a few seconds, where 00:00:13.259 --> 00:00:15.500 a single three -digit number flashes across a 00:00:15.500 --> 00:00:18.039 screen, and really, it essentially decides your 00:00:18.039 --> 00:00:20.100 financial fate. It dictates whether you're approved, 00:00:20.339 --> 00:00:21.980 whether you're rejected, or, and this is the 00:00:21.980 --> 00:00:24.379 most critical part, what the price of that money 00:00:24.379 --> 00:00:27.460 is going to be. You came to us asking to demystify 00:00:27.460 --> 00:00:30.390 one of the most powerful yet persistently opaque 00:00:30.390 --> 00:00:33.450 mechanisms in modern life the credit score and 00:00:33.450 --> 00:00:36.509 the you know the history it's built upon Okay, 00:00:36.570 --> 00:00:39.469 let's unpack this. Our mission today is to dive 00:00:39.469 --> 00:00:41.530 deep into the fundamentals. We aren't just looking 00:00:41.530 --> 00:00:43.990 at the famous five factors, but the actual math, 00:00:44.130 --> 00:00:47.350 the surprising history, and the huge real -world 00:00:47.350 --> 00:00:49.509 consequences this financial fingerprint has for 00:00:49.509 --> 00:00:51.929 both us as consumers and for the lenders who 00:00:51.929 --> 00:00:54.149 use it as their primary oracle. We need to know 00:00:54.149 --> 00:00:55.929 not just what the score is, but what it's for. 00:00:56.170 --> 00:00:58.729 And to really understand that, Angel, we have 00:00:58.729 --> 00:01:02.070 to start with the foundational definitions. We 00:01:02.070 --> 00:01:03.509 get these straight from the source material. 00:01:03.530 --> 00:01:05.150 We talk about credit history and credit scores. 00:01:05.579 --> 00:01:08.219 interchangeably, but they are. They're very distinct 00:01:08.219 --> 00:01:11.480 concepts. So a credit history, that's the simple 00:01:11.480 --> 00:01:13.780 foundational record. It's just a verifiable account 00:01:13.780 --> 00:01:17.040 of a borrower's responsible repayment of debts 00:01:17.040 --> 00:01:20.159 over time. That record is then compiled into 00:01:20.159 --> 00:01:22.400 a massive document called the credit report. 00:01:22.680 --> 00:01:24.519 And that's the thing the lenders actually see. 00:01:24.680 --> 00:01:27.019 Exactly. The report is the comprehensive file. 00:01:27.140 --> 00:01:29.260 It pulls data from all over the place. Banks, 00:01:29.620 --> 00:01:31.799 credit card companies, collection agencies, debt 00:01:31.799 --> 00:01:34.819 buyers, and even... certain government records 00:01:34.819 --> 00:01:38.159 about judgments or liens. So the history is the 00:01:38.159 --> 00:01:40.760 behavior and the report is the file. That brings 00:01:40.760 --> 00:01:43.819 us to the score itself. What precisely is the 00:01:43.819 --> 00:01:45.799 credit score doing with all that information? 00:01:46.159 --> 00:01:48.719 The credit score, that three -digit number, is 00:01:48.719 --> 00:01:51.200 the result of applying a really complex mathematical 00:01:51.200 --> 00:01:54.879 algorithm to that comprehensive report. And this 00:01:54.879 --> 00:01:57.239 is crucial. Its entire reason for being, its 00:01:57.239 --> 00:01:59.319 mission statement, if you will, is to predict 00:01:59.319 --> 00:02:01.950 future delinquency. So it's not a report card 00:02:01.950 --> 00:02:04.510 of your past. It's a crystal ball for your future. 00:02:04.689 --> 00:02:07.170 That's a great way to put it. It is not a measure 00:02:07.170 --> 00:02:10.469 of wealth or your income or your overall financial 00:02:10.469 --> 00:02:13.870 stability. It is purely a statistical forecast 00:02:13.870 --> 00:02:16.930 of the probability that you will default on a 00:02:16.930 --> 00:02:19.789 debt in the next 24 months compared to the average 00:02:19.789 --> 00:02:23.069 consumer. Wow. That distinction prediction versus 00:02:23.069 --> 00:02:26.419 summary. That's key. So when you approach a lender, 00:02:26.560 --> 00:02:29.439 say for a big purchase like a car, your info 00:02:29.439 --> 00:02:31.900 goes to a credit bureau to determine your credit 00:02:31.900 --> 00:02:34.240 worthiness. Lenders are really just trying to 00:02:34.240 --> 00:02:36.460 satisfy two basic requirements. First, your ability 00:02:36.460 --> 00:02:39.319 to repay the debt. Which is your income, your 00:02:39.319 --> 00:02:41.259 job stability, that kind of thing. Right. But 00:02:41.259 --> 00:02:43.039 second, and this is where the report is solely 00:02:43.039 --> 00:02:46.439 responsible, is your willingness to repay. which 00:02:46.439 --> 00:02:49.020 is indicated by that long history of timely, 00:02:49.180 --> 00:02:51.319 unmissed past payments. And that willingness 00:02:51.319 --> 00:02:53.939 aspect is where the report is absolutely ruthless. 00:02:54.139 --> 00:02:56.719 The scoring models are just. They're laser -focused 00:02:56.719 --> 00:02:59.860 on identifying risk indicators. The sources really 00:02:59.860 --> 00:03:02.099 emphasize this. You can be the most diligent 00:03:02.099 --> 00:03:04.199 saver in the world. You can make overpayments 00:03:04.199 --> 00:03:06.539 on one account, but none of that offsets a missed 00:03:06.539 --> 00:03:09.020 payment on another. So one slip -up can erase 00:03:09.020 --> 00:03:12.000 a lot of good behavior. It really can. Missed 00:03:12.000 --> 00:03:14.300 payments? The failures to meet your contractual 00:03:14.300 --> 00:03:17.400 obligations are the primary focus of these risk 00:03:17.400 --> 00:03:19.719 assessment algorithms because they're the clearest 00:03:19.719 --> 00:03:22.580 signal of potential future default. Okay, so 00:03:22.580 --> 00:03:24.800 let's get into why this predictive score holds 00:03:24.800 --> 00:03:27.319 so much power today. Let's delve into that because 00:03:27.319 --> 00:03:29.840 for decades, a credit report just helped a lender 00:03:29.840 --> 00:03:31.840 decide if they should give you credit, yes or 00:03:31.840 --> 00:03:35.099 no. Today, with the widespread adoption of what's 00:03:35.099 --> 00:03:37.620 called risk -based pricing across the entire 00:03:37.620 --> 00:03:40.620 financial services industry, the score's influence 00:03:40.620 --> 00:03:43.960 has just exploded. That term, risk -based pricing, 00:03:44.199 --> 00:03:46.620 it sounds very academic, but for us, the consumer, 00:03:46.860 --> 00:03:48.939 it's just brutal reality. What does that actually 00:03:48.939 --> 00:03:51.219 mean in practice? It means the credit report 00:03:51.219 --> 00:03:53.860 often becomes the sole element used to determine 00:03:53.860 --> 00:03:56.099 the core contractual obligations of your credit 00:03:56.099 --> 00:03:59.460 card or your loan. So not just yes or no, but 00:03:59.460 --> 00:04:02.919 how much it will cost you. Exactly. This determines 00:04:02.919 --> 00:04:05.879 the annual percentage rate, the APR, the length 00:04:05.879 --> 00:04:08.180 of the grace period, and sometimes even the size 00:04:08.180 --> 00:04:10.539 of the loan you qualify for. It is the financial 00:04:10.539 --> 00:04:13.680 services industry's mechanism for differentiating 00:04:13.680 --> 00:04:16.019 the cost of money based on your statistical profile. 00:04:16.300 --> 00:04:18.939 Let's make that concrete. The score isn't just 00:04:18.939 --> 00:04:21.300 a gatekeeper for approval anymore. It's a direct 00:04:21.300 --> 00:04:23.980 determinant of your future wealth. Precisely. 00:04:23.980 --> 00:04:26.139 Let's put some numbers to it. Imagine you're 00:04:26.139 --> 00:04:30.000 applying for a, say, a $300 ,000 mortgage. If 00:04:30.000 --> 00:04:33.500 your FICO score is prime, maybe a 750, you might 00:04:33.500 --> 00:04:37.060 qualify for an APR of 4 .5%. Okay, pretty standard. 00:04:37.240 --> 00:04:39.519 But if your score is subprime, let's say 650, 00:04:39.920 --> 00:04:42.600 maybe because of a few late payments, your APR 00:04:42.600 --> 00:04:45.100 could jump to 6 .5%. That two -point difference 00:04:45.100 --> 00:04:47.459 sounds small, but over 30 years on a mortgage. 00:04:47.579 --> 00:04:51.560 It's massive. On that $300 ,000 loan, that difference 00:04:51.560 --> 00:04:54.220 in the interest rate translates to tens of thousands 00:04:54.220 --> 00:04:56.339 of dollars in extra interest paid over the life 00:04:56.339 --> 00:04:58.759 of the mortgage. Wow. That money is purely the 00:04:58.759 --> 00:05:01.040 premium you pay for being categorized as a higher 00:05:01.040 --> 00:05:03.939 statistical risk. The so what here is that this 00:05:03.939 --> 00:05:06.279 score dictates how much money you save or lose 00:05:06.279 --> 00:05:09.339 over decades. But if this mechanism holds so 00:05:09.339 --> 00:05:11.899 much financial leverage over millions of us, 00:05:12.000 --> 00:05:15.079 we absolutely have to challenge the data integrity 00:05:15.079 --> 00:05:17.920 behind it. And what we find in the source material 00:05:17.920 --> 00:05:20.560 is a serious conflict between what the industry 00:05:20.560 --> 00:05:23.620 says and what consumers experience when it comes 00:05:23.620 --> 00:05:27.560 to accuracy. That tension is palpable. The industry, 00:05:27.579 --> 00:05:31.240 represented by the big credit bureaus, they staunchly 00:05:31.240 --> 00:05:33.819 maintain that their data is very accurate. They 00:05:33.819 --> 00:05:36.040 cite these studies, including one that looked 00:05:36.040 --> 00:05:38.610 at a massive... sample of 52 million credit reports 00:05:38.610 --> 00:05:41.069 and they claim it's all reliable. And what did 00:05:41.069 --> 00:05:43.569 they tell Congress? Well, when they testified 00:05:43.569 --> 00:05:45.569 before the U .S. Congress, they asserted that 00:05:45.569 --> 00:05:48.389 less than 2 percent of disputed data was ultimately 00:05:48.389 --> 00:05:50.370 deleted because it was found to be an error. 00:05:50.550 --> 00:05:53.250 OK, that's the industry narrative. 98 percent 00:05:53.250 --> 00:05:56.470 accuracy. But we have to inject some critical 00:05:56.470 --> 00:05:58.629 thinking here. If we accept their own number 00:05:58.629 --> 00:06:02.009 of 52 million reports. 2 % of that is still over 00:06:02.009 --> 00:06:05.009 1 million reports containing errors. Errors that, 00:06:05.110 --> 00:06:07.490 when disputed, were serious enough to be deleted. 00:06:07.689 --> 00:06:10.430 So to an individual consumer, an error isn't 00:06:10.430 --> 00:06:13.529 a 2 % problem, it's a 100 % problem, especially 00:06:13.529 --> 00:06:15.870 if it costs them that lower mortgage rate. And 00:06:15.870 --> 00:06:18.089 that's the core of the consumer concern, which 00:06:18.089 --> 00:06:21.029 the sources highlight as widespread. Despite 00:06:21.029 --> 00:06:23.990 these industry claims of high accuracy, the perception 00:06:23.990 --> 00:06:27.050 that information is prone to error is pervasive. 00:06:27.709 --> 00:06:30.740 This perception was so strong... and the instances 00:06:30.740 --> 00:06:33.579 of individual catastrophic error were so frequent 00:06:33.579 --> 00:06:35.959 that the U .S. Congress was compelled to enact 00:06:35.959 --> 00:06:38.730 specific laws. like the Fair Credit Reporting 00:06:38.730 --> 00:06:41.629 Act, designed specifically to resolve both the 00:06:41.629 --> 00:06:44.310 actual data errors and this profound perception 00:06:44.310 --> 00:06:47.149 of errors. Which makes the dispute process the 00:06:47.149 --> 00:06:50.110 consumer's only real safeguard. Exactly. If a 00:06:50.110 --> 00:06:51.970 U .S. consumer identifies information on their 00:06:51.970 --> 00:06:53.689 credit report they believe is wrong and they 00:06:53.689 --> 00:06:55.870 start a dispute, the credit bureau is legally 00:06:55.870 --> 00:06:58.410 mandated to verify that data with the creditor 00:06:58.410 --> 00:07:00.189 that reported it. And they have a deadline for 00:07:00.189 --> 00:07:03.069 that? They do. By federal law, they have 30 days 00:07:03.069 --> 00:07:05.430 to complete this verification process. And what 00:07:05.430 --> 00:07:07.600 do the metrics show? Are they meeting that? They're 00:07:07.600 --> 00:07:09.959 often quite efficient, actually. The Federal 00:07:09.959 --> 00:07:12.600 Trade Commission notes that over 70 % of these 00:07:12.600 --> 00:07:14.800 consumer disputes are resolved significantly 00:07:14.800 --> 00:07:18.920 faster, usually within just 14 days. The consumer 00:07:18.920 --> 00:07:22.100 is then notified of what they found. And interestingly, 00:07:22.339 --> 00:07:24.439 there's an internal statistic from one of the 00:07:24.439 --> 00:07:27.920 large credit bureaus that claims 95 % of those 00:07:27.920 --> 00:07:30.740 who dispute an item are satisfied with the outcome. 00:07:31.240 --> 00:07:33.560 Which could mean it was removed, or could just 00:07:33.560 --> 00:07:35.819 mean they finally got an explanation. It suggests 00:07:35.819 --> 00:07:38.160 that even if the item wasn't removed, the process 00:07:38.160 --> 00:07:40.660 of communicating the resolution, or proving the 00:07:40.660 --> 00:07:43.420 data was indeed correct, satisfied the consumer's 00:07:43.420 --> 00:07:45.920 need for answers. It's a system designed to be 00:07:45.920 --> 00:07:48.480 self -correcting, at least in theory, albeit 00:07:48.480 --> 00:07:51.920 imperfectly. The score is the ultimate arbiter, 00:07:51.939 --> 00:07:54.519 so understanding what feeds that engine is essential. 00:07:55.040 --> 00:07:57.100 So while there are competing scoring models out 00:07:57.100 --> 00:07:59.939 there like Vantage Score in the U .S., in Canada, 00:08:00.040 --> 00:08:02.019 and in a lot of international contexts, the FICO 00:08:02.019 --> 00:08:04.860 scoring system is still the undisputed standard. 00:08:05.120 --> 00:08:07.720 It's used by the vast majority of prime lenders. 00:08:08.040 --> 00:08:10.759 If you manage your FICO score, you are managing 00:08:10.759 --> 00:08:13.379 your financial identity. Here's where it gets 00:08:13.379 --> 00:08:14.819 really interesting. We're going to break down 00:08:14.819 --> 00:08:17.709 the five weighted categories. And understanding 00:08:17.709 --> 00:08:20.569 the weight isn't just information. It's an instruction 00:08:20.569 --> 00:08:23.069 manual for managing your financial life. We have 00:08:23.069 --> 00:08:25.529 to start with the largest component, the absolute 00:08:25.529 --> 00:08:28.810 foundation of the scoring model, Pillar 1, payment 00:08:28.810 --> 00:08:31.870 history. This contributes a massive 35 % to your 00:08:31.870 --> 00:08:33.950 total FICO score. A third of the whole thing. 00:08:34.090 --> 00:08:36.149 Over a third. If you want to know the single 00:08:36.149 --> 00:08:37.909 most important action you can take to control 00:08:37.909 --> 00:08:41.309 your score, it is never, ever, under any circumstance, 00:08:41.570 --> 00:08:44.279 miss a payment. Since the score is designed to 00:08:44.279 --> 00:08:46.799 predict future delinquency, your history of past 00:08:46.799 --> 00:08:49.200 delinquency is the single most important indicator 00:08:49.200 --> 00:08:51.860 they have. So what specifically is FICO looking 00:08:51.860 --> 00:08:55.039 for in this 35 %? What are the biggest danger 00:08:55.039 --> 00:08:57.779 flags? They're looking for severe negative items. 00:08:58.299 --> 00:09:00.720 Things like bankruptcies, collections, charge 00:09:00.720 --> 00:09:02.679 -offs. A charge -off is when a creditor just 00:09:02.679 --> 00:09:05.230 gives up on... collecting a debt, right? Exactly. 00:09:05.450 --> 00:09:07.950 They write it off as a loss. So charge -offs, 00:09:08.049 --> 00:09:10.629 foreclosures, repossessions, settlements, liens, 00:09:10.649 --> 00:09:12.909 and of course the most common ones, late and 00:09:12.909 --> 00:09:16.279 missed payments. The existence of any of these 00:09:16.279 --> 00:09:19.080 will negatively impact the score, often drastically. 00:09:19.399 --> 00:09:21.899 But within that 35%, there's some crucial nuance. 00:09:22.200 --> 00:09:24.519 A lot of people might assume a major historical 00:09:24.519 --> 00:09:26.759 event like a bankruptcy from eight years ago 00:09:26.759 --> 00:09:29.279 is the worst thing on their file. But the sources 00:09:29.279 --> 00:09:32.019 suggest recency and severity are locked in this 00:09:32.019 --> 00:09:34.559 kind of dance. That's absolutely correct. FICO 00:09:34.559 --> 00:09:37.299 models look at three subfactors within this payment 00:09:37.299 --> 00:09:40.220 history category. First is the severity of the 00:09:40.220 --> 00:09:43.940 item. So a 120 -day late payment is worse than 00:09:43.940 --> 00:09:46.480 a 30 -day late payment, and a bankruptcy is worse 00:09:46.480 --> 00:09:49.179 than both. That's straightforward. Second is 00:09:49.179 --> 00:09:52.100 the age of the negative item. A recent 30 -day 00:09:52.100 --> 00:09:54.679 late payment, even on a small credit card, is 00:09:54.679 --> 00:09:57.179 often more damaging to your current score than 00:09:57.179 --> 00:09:58.899 a 15 -year -old collection account that's about 00:09:58.899 --> 00:10:01.259 to fall off your report entirely. Because recent 00:10:01.259 --> 00:10:03.700 behavior is seen as a better predictor of immediate 00:10:03.700 --> 00:10:07.000 future behavior. Precisely. Recent unpaid debt 00:10:07.000 --> 00:10:10.419 signals immediate risk. And third factor. Prevalence. 00:10:10.779 --> 00:10:13.360 Prevalence. Are there multiple instances of problems? 00:10:13.519 --> 00:10:16.799 Is this a single isolated mistake you made during 00:10:16.799 --> 00:10:18.940 a tough time, or is there a pattern of missed 00:10:18.940 --> 00:10:21.639 payments across multiple creditors? A pervasive 00:10:21.639 --> 00:10:24.279 pattern of risk just accelerates the score drop. 00:10:24.980 --> 00:10:27.220 We should also note that the modern FICO models 00:10:27.220 --> 00:10:29.820 have slightly altered their calculation. Since 00:10:29.820 --> 00:10:32.080 FICO 9, for example, unpaid medical collections 00:10:32.080 --> 00:10:34.759 are often weighted less heavily and paid collections 00:10:34.759 --> 00:10:36.620 are generally removed from the calculation entirely. 00:10:36.980 --> 00:10:39.200 That was a huge shift that helped a lot of people. 00:10:39.379 --> 00:10:42.019 A huge shift. Yeah. It benefited many consumers. 00:10:42.299 --> 00:10:44.960 So that change in how medical debt is treated 00:10:44.960 --> 00:10:47.039 is a perfect example of the system adapting. 00:10:47.399 --> 00:10:50.120 If you manage that 35 percent, you've mastered 00:10:50.120 --> 00:10:52.740 the first hurdle. But that leaves the second 00:10:52.740 --> 00:10:55.500 most important piece, which feels like pure mathematics. 00:10:55.919 --> 00:10:59.679 Pillar two, debt or amounts owed. This makes 00:10:59.679 --> 00:11:03.019 up a whopping 30 % of your score. Yep. So when 00:11:03.019 --> 00:11:05.460 you combine it with timely payments, we are now 00:11:05.460 --> 00:11:08.240 accounting for 65 % of your total score. Just 00:11:08.240 --> 00:11:11.789 those two things. 65%. Okay. The central unavoidable 00:11:11.789 --> 00:11:14.330 metric here is the credit utilization ratio. 00:11:14.809 --> 00:11:17.269 Sometimes it's called a capacity ratio. It is 00:11:17.269 --> 00:11:20.210 just a mathematical expression of your dependency 00:11:20.210 --> 00:11:22.490 on your credit limits. So it's the amount of 00:11:22.490 --> 00:11:25.210 debt you carry divided by your total credit limit. 00:11:25.350 --> 00:11:28.029 Simple as that. Low utilization ratio indicates 00:11:28.029 --> 00:11:30.250 you're financially secure and you're not reliant 00:11:30.250 --> 00:11:32.929 on borrowing, which is exactly what lenders want 00:11:32.929 --> 00:11:35.230 to see. Now, we have to be clear that the algorithm 00:11:35.230 --> 00:11:37.490 pays overwhelming attention to revolving debt. 00:11:37.919 --> 00:11:40.019 Can you define that for us and explain why it's 00:11:40.019 --> 00:11:44.000 prioritized over, say, a mortgage? Sure. Revolving 00:11:44.000 --> 00:11:46.759 debt is the credit you can use, pay down, and 00:11:46.759 --> 00:11:49.679 then use again. So primarily credit card debt, 00:11:49.799 --> 00:11:51.960 retail card debt, and other unsecured lines of 00:11:51.960 --> 00:11:54.779 credit. A mortgage or an auto loan, on the other 00:11:54.779 --> 00:11:57.580 hand, is installment debt. It's a fixed payment 00:11:57.580 --> 00:11:59.980 schedule over a fixed term, and it's usually 00:11:59.980 --> 00:12:02.220 secured by an asset like the house or the car. 00:12:02.539 --> 00:12:04.899 And because installment debt is secured, the 00:12:04.899 --> 00:12:07.299 risk to the lender is lower. It's much lower. 00:12:07.639 --> 00:12:09.779 and therefore the algorithm treats revolving 00:12:09.779 --> 00:12:13.500 utilization as far more important. The most critical 00:12:13.500 --> 00:12:16.059 measurement here is what the scoring systems 00:12:16.059 --> 00:12:19.840 call revolving utilization. This is the aggregate 00:12:19.840 --> 00:12:22.480 of all your credit card balances divided by the 00:12:22.480 --> 00:12:24.480 aggregate of your total available credit card 00:12:24.480 --> 00:12:27.379 limits expressed as a percentage. So, for example, 00:12:27.440 --> 00:12:29.539 if you have two cards with total balances of 00:12:29.539 --> 00:12:33.179 $1 ,000 and total limits of $10 ,000, your utilization 00:12:33.179 --> 00:12:36.019 is 10%. Perfect example. And you're in great 00:12:36.019 --> 00:12:38.059 shape. And the utilization warning we constantly 00:12:38.059 --> 00:12:40.580 hear is this. That higher percentage drastically 00:12:40.580 --> 00:12:43.100 lowers the score. Where is the cliff edge on 00:12:43.100 --> 00:12:45.620 that? Well, the general advice is always to stay 00:12:45.620 --> 00:12:48.580 under 30 % utilization. If you cross that 30 00:12:48.580 --> 00:12:51.000 % threshold, you should expect a significant 00:12:51.000 --> 00:12:53.639 score penalty. But the sweet spot is much lower. 00:12:53.840 --> 00:12:56.279 Oh, yeah. The true sweet spot, where you receive 00:12:56.279 --> 00:12:59.159 the maximum points, is keeping utilization under 00:12:59.159 --> 00:13:03.210 10 % or even below 6%. The algorithms reward 00:13:03.210 --> 00:13:06.649 consumers who barely use their available credit. 00:13:06.970 --> 00:13:10.210 This brings up a critical counterintuitive point 00:13:10.210 --> 00:13:12.590 that trips up so many people trying to simplify 00:13:12.590 --> 00:13:15.009 their finances. They think, OK, I'll close this 00:13:15.009 --> 00:13:17.830 unused credit card to tidy up my file. But what 00:13:17.830 --> 00:13:20.750 happens then? That is generally a terrible idea 00:13:20.750 --> 00:13:23.789 if your goal is to boost your score. When you 00:13:23.789 --> 00:13:26.210 close a credit card, you instantly reduce your 00:13:26.210 --> 00:13:28.809 total available credit limits. That's the denominator 00:13:28.809 --> 00:13:31.169 in the utilization ratio equation. Right. So 00:13:31.169 --> 00:13:33.490 unless you simultaneously pay down your existing 00:13:33.490 --> 00:13:36.169 debt by the same amount, your utilization percent 00:13:36.299 --> 00:13:38.620 automatically spikes up and it punishes your 00:13:38.620 --> 00:13:41.480 score immediately so keeping those older unused 00:13:41.480 --> 00:13:43.460 cards open even if they're hidden in a drawer 00:13:43.460 --> 00:13:46.899 actually helps you it helps keep that total limit 00:13:46.899 --> 00:13:50.600 denominator large and crucially your utilization 00:13:50.600 --> 00:13:54.419 percentage low it's all just math so if someone 00:13:54.419 --> 00:13:57.240 has zero balances and has mastered that 35 % 00:13:57.240 --> 00:13:59.799 they should have a perfect score right Wait, 00:13:59.820 --> 00:14:02.620 no, because that 65 % doesn't account for the 00:14:02.620 --> 00:14:04.440 simple fact of how long they've been doing this. 00:14:04.539 --> 00:14:07.620 That's pillar three. Exactly. We move now to 00:14:07.620 --> 00:14:12.019 stability and time. Pillar three, time and file 00:14:12.019 --> 00:14:15.740 age. This factor contributes 15 % to the FICO 00:14:15.740 --> 00:14:18.279 score. The logic here seems pretty straightforward. 00:14:18.909 --> 00:14:21.149 Older files demonstrate greater statistical inertia. 00:14:21.710 --> 00:14:23.830 They're more stable and stability is rewarded. 00:14:24.129 --> 00:14:26.309 Right. And age is determined by two metrics, 00:14:26.509 --> 00:14:28.610 which combine to create a picture of what lenders 00:14:28.610 --> 00:14:32.009 call a thick file. First is the overall age of 00:14:32.009 --> 00:14:33.769 your credit file, which is determined by the 00:14:33.769 --> 00:14:36.309 date opened of the oldest account still reporting 00:14:36.309 --> 00:14:38.450 on your file. So if you opened a card 20 years 00:14:38.450 --> 00:14:40.570 ago and it's still open, that's your primary 00:14:40.570 --> 00:14:42.889 history marker. That's your anchor. But the second 00:14:42.889 --> 00:14:44.690 metric looks at everything else. The average 00:14:44.690 --> 00:14:47.629 age of all accounts on the credit report. whether 00:14:47.629 --> 00:14:49.269 those accounts are currently open or closed. 00:14:49.769 --> 00:14:51.950 So if you have a few accounts that are 20 years 00:14:51.950 --> 00:14:54.330 old, but you opened 10 new accounts last year, 00:14:54.450 --> 00:14:57.789 that average age will drop significantly, and 00:14:57.789 --> 00:15:00.370 it'll slightly penalize your score because your 00:15:00.370 --> 00:15:02.590 cumulative history now appears less seasoned. 00:15:02.769 --> 00:15:05.850 Okay. Next, we have a category that rewards financial 00:15:05.850 --> 00:15:08.690 versatility. It suggests that if you can handle 00:15:08.690 --> 00:15:10.970 multiple types of debt, you're less of a risk. 00:15:11.149 --> 00:15:15.000 Pillar four. account diversity, or types of credit. 00:15:15.299 --> 00:15:18.120 This only accounts for 10 % of the score, but 00:15:18.120 --> 00:15:20.139 it's still valuable, especially for thin files. 00:15:20.419 --> 00:15:22.419 Yeah, having a diverse credit mix on your file 00:15:22.419 --> 00:15:25.139 benefits your score. This means demonstrating 00:15:25.139 --> 00:15:27.519 experience managing both revolving accounts, 00:15:27.740 --> 00:15:30.460 like credit cards, and installment loans, like 00:15:30.460 --> 00:15:33.259 auto debt or student loans. If you can prove 00:15:33.259 --> 00:15:35.460 the ability to successfully manage these different 00:15:35.460 --> 00:15:37.940 financial products at the same time, the score 00:15:37.940 --> 00:15:40.500 is rewarded. A consumer with just three credit 00:15:40.500 --> 00:15:43.100 cards often scores lower than a consumer who 00:15:43.100 --> 00:15:45.299 has three credit cards and a successfully managed 00:15:45.299 --> 00:15:47.679 auto loan in their history, even if both have 00:15:47.679 --> 00:15:50.480 perfect payment records. That makes sense. And 00:15:50.480 --> 00:15:53.080 that brings us to the final 10%, which often 00:15:53.080 --> 00:15:57.799 causes the most consumer anxiety. Pillar 5. The 00:15:57.799 --> 00:16:00.539 search for new credit inquiries. This makes up 00:16:00.539 --> 00:16:03.409 the final 10 % contribution. And inquiries are 00:16:03.409 --> 00:16:05.470 recorded every time a company requests information 00:16:05.470 --> 00:16:08.409 from your credit file. But as we touched on briefly 00:16:08.409 --> 00:16:11.730 earlier, this 10 % requires a deep dive into 00:16:11.730 --> 00:16:14.490 the two distinct types of inquiries. We have 00:16:14.490 --> 00:16:17.149 to distinguish them clearly because one has absolutely 00:16:17.149 --> 00:16:19.750 no effect on your credit worthiness and the other 00:16:19.750 --> 00:16:22.690 can cause that small temporary drop. Okay, let's 00:16:22.690 --> 00:16:25.590 start with the benign ones, soft inquiries. These 00:16:25.590 --> 00:16:27.909 are invisible to lenders and credit scoring models. 00:16:28.169 --> 00:16:30.110 They stay on your report for about six months. 00:16:30.570 --> 00:16:32.610 But a lender pulling your file will never see 00:16:32.610 --> 00:16:34.710 them, and they cannot affect your score. Right. 00:16:35.090 --> 00:16:37.490 Soft inquiries cover several common situations. 00:16:37.950 --> 00:16:40.570 The source material is very specific here. These 00:16:40.570 --> 00:16:42.789 include pre -screening inquiries. Those are the 00:16:42.789 --> 00:16:44.529 pre -approved credit card offers that show up 00:16:44.529 --> 00:16:46.669 in the mail. That's them. The credit bureau sells 00:16:46.669 --> 00:16:48.870 your contact information to a company that then 00:16:48.870 --> 00:16:51.549 sends you offers. You didn't ask for the credit 00:16:51.549 --> 00:16:53.710 yet, but the lender checked your general profile. 00:16:54.190 --> 00:16:56.490 We also see them when our existing creditors 00:16:56.490 --> 00:16:58.730 check up on us. Yes, that's called account management 00:16:58.730 --> 00:17:01.730 or review. Your current credit card company or 00:17:01.730 --> 00:17:04.390 bank periodically checks your file to make sure 00:17:04.390 --> 00:17:07.230 you still fit their risk profile. Maybe they're 00:17:07.230 --> 00:17:09.190 deciding whether to raise your limit or decrease 00:17:09.190 --> 00:17:11.430 it. And checking your own credit, that's a soft 00:17:11.430 --> 00:17:13.970 pull too, right? Always. If you check your own 00:17:13.970 --> 00:17:17.009 report, a consumer disclosure inquiry, that's 00:17:17.009 --> 00:17:19.109 a soft pull. And beyond the financial world, 00:17:19.349 --> 00:17:21.509 soft pulls are also common for background checks. 00:17:21.769 --> 00:17:24.759 Exactly. Employment screening inquiries, utility 00:17:24.759 --> 00:17:27.500 -related inquiries, when you open an electricity 00:17:27.500 --> 00:17:29.640 account, for example, and insurance -related 00:17:29.640 --> 00:17:31.980 inquiries are all considered softballs and do 00:17:31.980 --> 00:17:35.119 not negatively affect your score, though they 00:17:35.119 --> 00:17:37.980 do require your permission. Now for the inquiries 00:17:37.980 --> 00:17:41.279 that matter to that 10 % pillar, the hard inquiries. 00:17:41.400 --> 00:17:43.700 These are the ones where you, the consumer, have 00:17:43.700 --> 00:17:46.019 actively initiated a search for credit. Right. 00:17:46.319 --> 00:17:48.819 Hard inquiries are made by lenders. When you 00:17:48.819 --> 00:17:51.460 formally apply for a new credit card, a mortgage, 00:17:51.640 --> 00:17:54.819 an auto loan, or any extension of credit, they 00:17:54.819 --> 00:17:57.119 require what's called a permissible purpose, 00:17:57.319 --> 00:17:59.700 as defined by the Fair Credit Reporting Act. 00:18:00.579 --> 00:18:02.720 Basically, you gave them explicit permission 00:18:02.720 --> 00:18:05.240 to check your file because you were seeking money. 00:18:05.400 --> 00:18:07.839 So what's the mechanical consequence of one of 00:18:07.839 --> 00:18:10.660 these? A single hard inquiry typically causes 00:18:10.660 --> 00:18:13.559 a small temporary drop -off, just a few points. 00:18:14.119 --> 00:18:16.390 However... The larger concern that feeds the 00:18:16.390 --> 00:18:19.190 10 % calculation is the concentration of these 00:18:19.190 --> 00:18:22.009 inquiries. A large number of inquiries over a 00:18:22.009 --> 00:18:24.789 short period might signal to a lender that the 00:18:24.789 --> 00:18:26.869 person is in financial distress, that they're 00:18:26.869 --> 00:18:29.049 desperate for money, and therefore a poor credit 00:18:29.049 --> 00:18:31.750 risk. But this is where the nuance of rate shopping 00:18:31.750 --> 00:18:33.829 comes in. And this is a crucial piece of knowledge 00:18:33.829 --> 00:18:35.849 for any consumer getting a mortgage or a car 00:18:35.849 --> 00:18:38.410 loan. If I apply to 10 different mortgage brokers 00:18:38.410 --> 00:18:40.390 in a week to find the best rate, I don't get 00:18:40.390 --> 00:18:42.910 10 separate drops on my score, do I? You absolutely 00:18:42.910 --> 00:18:45.869 do not. The scoring models are smart enough to 00:18:45.869 --> 00:18:48.349 distinguish between a consumer shopping for the 00:18:48.349 --> 00:18:51.369 best rate on a single large loan and a consumer 00:18:51.369 --> 00:18:53.769 desperately trying to get five new credit cards. 00:18:54.170 --> 00:18:57.849 This is called inquiry deduplication or rate 00:18:57.849 --> 00:19:00.819 shopping. How does that system work? For large 00:19:00.819 --> 00:19:03.980 installment loans, specifically mortgages, auto 00:19:03.980 --> 00:19:07.140 loans, and student loans, FICO gives you a specific 00:19:07.140 --> 00:19:11.220 window of time, typically 14 to 45 days, during 00:19:11.220 --> 00:19:13.859 which all related inquiries for that single purpose 00:19:13.859 --> 00:19:16.299 are counted as only one inquiry for scoring purposes. 00:19:16.640 --> 00:19:18.180 So you're actually encouraged to shop around. 00:19:18.440 --> 00:19:20.420 You are. You can shop around for the best car 00:19:20.420 --> 00:19:22.980 loan rate without fear of your score being unduly 00:19:22.980 --> 00:19:26.059 penalized multiple times. The algorithm recognizes 00:19:26.059 --> 00:19:28.890 this as responsible behavior. not risk behavior. 00:19:29.190 --> 00:19:31.349 That is a detail that changes everything about 00:19:31.349 --> 00:19:33.450 how you should approach that process. Now, connecting 00:19:33.450 --> 00:19:35.829 this whole history tracking system to a major 00:19:35.829 --> 00:19:38.990 global issue, what happens when you move? The 00:19:38.990 --> 00:19:41.589 problem of international mobility, or what some 00:19:41.589 --> 00:19:44.509 call the immigrant paradox, is a massive hurdle 00:19:44.509 --> 00:19:47.109 for highly mobile professionals. Credit history 00:19:47.109 --> 00:19:49.769 usually stays strictly within one country. Even 00:19:49.769 --> 00:19:52.369 if it's the same company? Even then. So if you 00:19:52.369 --> 00:19:54.869 move from the UK to the US, and both countries 00:19:54.869 --> 00:19:57.369 use a multinational credit bureau like Experian, 00:19:57.799 --> 00:20:00.660 The data files are generally segregated. They 00:20:00.660 --> 00:20:02.680 don't talk to each other. Which means if you 00:20:02.680 --> 00:20:05.700 move from Germany to the U .S. with millions 00:20:05.700 --> 00:20:08.460 in the bank and a perfect 20 -year history, when 00:20:08.460 --> 00:20:11.700 you apply for a credit card here, the lender 00:20:11.700 --> 00:20:15.650 sees you as having a thin file, no history. And 00:20:15.650 --> 00:20:18.230 you get denied or offered a secured card with 00:20:18.230 --> 00:20:20.950 a tiny limit. Exactly. People moving countries 00:20:20.950 --> 00:20:23.029 often have to establish their credit history 00:20:23.029 --> 00:20:26.329 completely from scratch. This makes it incredibly 00:20:26.329 --> 00:20:28.430 difficult to get standard financial products 00:20:28.430 --> 00:20:30.829 like mortgages or unsecured credit cards initially, 00:20:30.970 --> 00:20:34.009 despite having impeccable financial habits somewhere 00:20:34.009 --> 00:20:36.569 else. You're forced to start with small limit 00:20:36.569 --> 00:20:38.490 secured cards or other alternative mechanisms. 00:20:38.710 --> 00:20:40.829 Are there any exceptions or strategies mentioned 00:20:40.829 --> 00:20:42.630 in the source material for getting around this? 00:20:42.789 --> 00:20:45.690 Very few. The strategies focus on finding lenders 00:20:45.690 --> 00:20:48.009 who use alternative data. The notable corporate 00:20:48.009 --> 00:20:49.529 exception that's mentioned is American Express. 00:20:49.789 --> 00:20:52.250 They operate internationally and are often able 00:20:52.250 --> 00:20:55.029 to leverage your credit history from your country 00:20:55.029 --> 00:20:57.710 of origin to approve a card in your new location. 00:20:57.970 --> 00:21:00.170 And that can jumpstart your credit history without 00:21:00.170 --> 00:21:02.470 you having to start with a secured card. But 00:21:02.470 --> 00:21:05.210 beyond that, it's an uphill climb. Okay, so let's 00:21:05.210 --> 00:21:08.029 shift gears. What happens when a consumer struggles 00:21:08.029 --> 00:21:11.789 to maintain that crucial 35 % payment history 00:21:11.789 --> 00:21:14.599 pillar? That's when they enter the realm of adverse 00:21:14.599 --> 00:21:17.799 credit. This negative rating goes by many equally 00:21:17.799 --> 00:21:21.039 unpleasant names. Subprime credit history, non 00:21:21.039 --> 00:21:23.759 -status credit history, impaired credit history, 00:21:23.920 --> 00:21:26.680 or just plain bad credit. And adverse credit 00:21:26.680 --> 00:21:29.059 fundamentally is the result of repeated failures 00:21:29.059 --> 00:21:32.180 to meet debt obligations. Yes. It's created by 00:21:32.180 --> 00:21:34.700 repeated delinquencies, excessive late payments, 00:21:34.839 --> 00:21:37.299 or those negative public record entries like 00:21:37.299 --> 00:21:39.859 judgments, collections, or bankruptcy filings 00:21:39.859 --> 00:21:43.009 that cause the score to drop significantly. The 00:21:43.009 --> 00:21:44.950 data reported by creditors is comprehensive. 00:21:45.289 --> 00:21:47.849 It includes detailed account information, payment 00:21:47.849 --> 00:21:50.130 history, any aggressive actions they took to 00:21:50.130 --> 00:21:52.369 recover the debts, and all of that just dramatically 00:21:52.369 --> 00:21:54.769 lowers the score. And a point we have to underscore 00:21:54.769 --> 00:21:57.369 is that the credit reporting agencies themselves 00:21:57.369 --> 00:22:00.869 do not decide if a score is adverse. They just 00:22:00.869 --> 00:22:03.970 report the number. That's vital. The score is 00:22:03.970 --> 00:22:06.529 just a numerical range. Each individual lender 00:22:06.529 --> 00:22:09.789 or creditor has its own non -disclosed internal 00:22:09.789 --> 00:22:12.789 policy on what constitutes an acceptable score 00:22:12.789 --> 00:22:15.509 for their products. This is known as their risk 00:22:15.509 --> 00:22:18.630 appetite. So one lender's adverse is another's 00:22:18.630 --> 00:22:21.410 acceptable. Potentially. One subprime lender 00:22:21.410 --> 00:22:24.970 might consider a score under 620 adverse, while 00:22:24.970 --> 00:22:27.349 a major mortgage lender might consider a score 00:22:27.349 --> 00:22:30.509 under 740 to be subprime for their best rates. 00:22:31.029 --> 00:22:33.210 And they don't typically disclose these internal 00:22:33.210 --> 00:22:36.009 cutoffs for competitive reasons. But the lender 00:22:36.009 --> 00:22:38.410 is legally required to provide transparency if 00:22:38.410 --> 00:22:41.029 they deny you. Absolutely. In the U .S., under 00:22:41.029 --> 00:22:43.069 the Fair Credit Reporting Act, if a creditor 00:22:43.069 --> 00:22:45.369 denies you credit, they are immediately required 00:22:45.369 --> 00:22:47.589 to give you the specific reasons for the denial. 00:22:47.690 --> 00:22:49.589 Was it too many inquiries? High utilization, 00:22:49.950 --> 00:22:52.069 late payments. And they must also provide the 00:22:52.069 --> 00:22:54.049 name and address of the specific credit reporting 00:22:54.049 --> 00:22:56.569 agency that provided the data they used to make 00:22:56.569 --> 00:22:58.529 that decision. So you have a path to follow up. 00:22:58.839 --> 00:23:01.160 It gives you the ability to follow up and correct 00:23:01.160 --> 00:23:03.660 any potential errors. Let's talk about the financial 00:23:03.660 --> 00:23:06.220 consequences again, but with that cost difference 00:23:06.220 --> 00:23:09.079 perspective we established earlier. The most 00:23:09.079 --> 00:23:12.799 direct impact of adverse credit is a huge reduction 00:23:12.799 --> 00:23:15.299 in the likelihood of approval and, if you are 00:23:15.299 --> 00:23:18.680 approved, a significant, often crushing, increase 00:23:18.680 --> 00:23:21.799 in interest rates and fees. As we discussed with 00:23:21.799 --> 00:23:23.960 the mortgage example, the increased interest 00:23:23.960 --> 00:23:26.500 is a direct reflection of risk -based pricing. 00:23:26.970 --> 00:23:30.029 It's the mechanism used by the lender to offset 00:23:30.029 --> 00:23:32.789 the statistically higher rate of default within 00:23:32.789 --> 00:23:35.250 that low credit rating group. So the high -risk 00:23:35.250 --> 00:23:37.970 borrower is essentially subsidizing the higher 00:23:37.970 --> 00:23:40.289 cost of that risk portfolio. That's one way to 00:23:40.289 --> 00:23:42.349 look at it. The source material emphasizes that 00:23:42.349 --> 00:23:45.009 the consequence is less approval and dramatically 00:23:45.009 --> 00:23:47.849 unfavorable terms if credit is extended at all. 00:23:48.140 --> 00:23:50.359 But the consequences of adverse credit are no 00:23:50.359 --> 00:23:52.440 longer limited to the financial transaction itself. 00:23:52.759 --> 00:23:55.539 This score has become a hidden gatekeeper for 00:23:55.539 --> 00:23:58.039 basic necessities, especially in the U .S. They 00:23:58.039 --> 00:24:01.079 spill far outside the world of lending. Adverse 00:24:01.079 --> 00:24:03.980 credit can lead to the denial of insurance, housing 00:24:03.980 --> 00:24:07.930 and perhaps most strikingly, employment. The 00:24:07.930 --> 00:24:09.910 score moves from being a predictor of financial 00:24:09.910 --> 00:24:13.349 risk to a proxy for personal integrity and reliability. 00:24:13.730 --> 00:24:15.849 The employment detail is particularly complex. 00:24:16.190 --> 00:24:19.869 The source material cited a 2013 survey focusing 00:24:19.869 --> 00:24:22.589 on just how pervasive this hidden check has become. 00:24:22.789 --> 00:24:24.970 That survey found that employer credit checks 00:24:24.970 --> 00:24:28.089 on job seekers were actively preventing people 00:24:28.089 --> 00:24:30.859 from reentering the workforce. At that time, 00:24:31.000 --> 00:24:33.240 one in four unemployed Americans were required 00:24:33.240 --> 00:24:35.000 to go through a credit check when applying for 00:24:35.000 --> 00:24:37.920 a job, particularly for positions involving handling 00:24:37.920 --> 00:24:40.680 money or sensitive information. So the employer 00:24:40.680 --> 00:24:43.200 is using financial health as a proxy for job 00:24:43.200 --> 00:24:45.500 trustworthiness. That's the idea. But that seems 00:24:45.500 --> 00:24:48.000 incredibly prejudicial, especially if the financial 00:24:48.000 --> 00:24:50.180 hardship was caused by something outside the 00:24:50.180 --> 00:24:52.160 applicant's control, like a medical emergency. 00:24:52.519 --> 00:24:55.000 It creates a tremendous societal friction point. 00:24:55.529 --> 00:24:58.750 Now, federal regulations under the FCRA require 00:24:58.750 --> 00:25:01.329 employers to receive permission from job candidates 00:25:01.329 --> 00:25:04.230 before running these checks. However, the source 00:25:04.230 --> 00:25:06.670 material notes that it can be incredibly difficult 00:25:06.670 --> 00:25:09.289 to prove that adverse credit history was the 00:25:09.289 --> 00:25:12.170 actual reason for the job denial, which allows 00:25:12.170 --> 00:25:15.160 employers a lot of leeway. Are there any protections 00:25:15.160 --> 00:25:17.900 against this? Some states like California, Illinois, 00:25:18.019 --> 00:25:20.900 and Washington have enacted specific laws to 00:25:20.900 --> 00:25:23.279 restrict or prohibit the use of credit checks 00:25:23.279 --> 00:25:26.400 for employment decisions unless the job is directly 00:25:26.400 --> 00:25:29.079 related to finance or security, acknowledging 00:25:29.079 --> 00:25:31.240 this systemic barrier. And the housing market 00:25:31.240 --> 00:25:33.200 operates in a similar way, right? Absolutely. 00:25:33.839 --> 00:25:36.000 Landlords and property management companies use 00:25:36.000 --> 00:25:38.059 the credit score to predict if you'll pay your 00:25:38.059 --> 00:25:40.660 rent on time. A low score might lead to a denial 00:25:40.660 --> 00:25:42.900 or demanding a much higher security deposit. 00:25:43.559 --> 00:25:45.759 Even insurance companies use a credit -based 00:25:45.759 --> 00:25:48.079 insurance score to set premiums, often arguing 00:25:48.079 --> 00:25:50.759 that individuals with lower credit scores file 00:25:50.759 --> 00:25:53.680 more claims. Your financial fingerprint permeates 00:25:53.680 --> 00:25:56.650 every aspect of your life. So this massive interconnected 00:25:56.650 --> 00:25:59.930 system reliant on data accuracy and statistical 00:25:59.930 --> 00:26:03.309 prediction is obviously a ripe target for exploitation. 00:26:03.690 --> 00:26:06.329 Our source material details abuse stemming from 00:26:06.329 --> 00:26:08.809 both the outside consumers or criminals exploiting 00:26:08.809 --> 00:26:11.529 weaknesses and the inside abuse committed by 00:26:11.529 --> 00:26:14.720 the credit reporting agencies themselves. Let's 00:26:14.720 --> 00:26:16.579 start with abuse by consumers and criminals. 00:26:17.380 --> 00:26:20.059 Astute individuals have identified and exploited 00:26:20.059 --> 00:26:21.859 vulnerabilities in the credit scoring systems 00:26:21.859 --> 00:26:24.440 to obtain credit they wouldn't otherwise qualify 00:26:24.440 --> 00:26:28.039 for or to maximize rewards. The document lists 00:26:28.039 --> 00:26:30.480 several recognized techniques used to game the 00:26:30.480 --> 00:26:33.539 system. One common technique is churning. which 00:26:33.539 --> 00:26:35.660 involves rapidly opening and closing accounts 00:26:35.660 --> 00:26:38.339 to maximize sign -up bonuses or rewards points. 00:26:38.539 --> 00:26:40.759 Okay. Another is rapid -fire credit applications, 00:26:41.039 --> 00:26:43.680 trying to submit applications so quickly that 00:26:43.680 --> 00:26:45.619 approvals are granted before the hard inquiries 00:26:45.619 --> 00:26:47.980 fully register and depress the score. Then there 00:26:47.980 --> 00:26:49.700 are techniques aimed at manipulating the history 00:26:49.700 --> 00:26:53.779 itself. Yes. This includes selective credit freezes, 00:26:54.160 --> 00:26:55.960 sometimes used in conjunction with applications, 00:26:56.339 --> 00:26:58.839 or applying for small business credit instead 00:26:58.839 --> 00:27:01.299 of personal credit because small business credit 00:27:01.299 --> 00:27:04.000 reporting is often less standardized or less 00:27:04.000 --> 00:27:06.519 stringent in the immediate term. And what about 00:27:06.519 --> 00:27:09.859 piggybacking? That sounds notorious. It is. Perhaps 00:27:09.859 --> 00:27:12.180 the most notorious technique is piggybacking. 00:27:12.200 --> 00:27:14.720 It's controversial because it works outside of 00:27:14.720 --> 00:27:17.519 typical credit behavior. It involves a person 00:27:17.519 --> 00:27:20.500 with a thin or damaged file convincing a friend 00:27:20.500 --> 00:27:23.079 or family member with excellent credit to add 00:27:23.079 --> 00:27:25.039 them to their account as an authorized user. 00:27:25.220 --> 00:27:27.279 And what happens when they get added as an authorized 00:27:27.279 --> 00:27:29.660 user? Instantly, the authorized user's credit 00:27:29.660 --> 00:27:32.380 file absorbs the entire history of the primary 00:27:32.380 --> 00:27:35.619 cardholder's account, the age, the limit, and 00:27:35.619 --> 00:27:37.920 the flawless payment history, even if they never 00:27:37.920 --> 00:27:40.400 actually use the card. Wow. So it just gives 00:27:40.400 --> 00:27:42.579 them an immediate boost. An immediate, often 00:27:42.579 --> 00:27:45.400 significant boost to their score, particularly... 00:27:45.480 --> 00:27:48.099 by lowering their utilization ratio and increasing 00:27:48.099 --> 00:27:51.000 their file age. This practice, while targeted 00:27:51.000 --> 00:27:53.440 by some FICO versions, is a well -known way to 00:27:53.440 --> 00:27:55.680 instantly manufacture a higher score. And then 00:27:55.680 --> 00:27:57.880 there is the macro -level vulnerability that 00:27:57.880 --> 00:28:00.640 affects all of us, data breaches. Absolutely. 00:28:00.920 --> 00:28:03.759 The source material specifically references the 00:28:03.759 --> 00:28:06.460 high -profile Equifax data breach, which was 00:28:06.460 --> 00:28:09.579 revealed in 2017 but spanned records from April 00:28:09.579 --> 00:28:12.240 to September of that year. This incident just 00:28:12.240 --> 00:28:14.400 underscored how vulnerable these massive data 00:28:14.400 --> 00:28:17.279 repositories are. Criminals gained access to 00:28:17.279 --> 00:28:19.380 the core financial identity data of millions. 00:28:19.579 --> 00:28:22.619 Social security numbers, dates of birth, addresses, 00:28:23.019 --> 00:28:25.380 driver's license numbers. The fallout from that 00:28:25.380 --> 00:28:27.819 breach was immense because unlike a compromised 00:28:27.819 --> 00:28:30.480 credit card, you can't simply change your social 00:28:30.480 --> 00:28:33.279 security number. Exactly. It exposed the entire 00:28:33.279 --> 00:28:35.819 infrastructure as a single point of failure. 00:28:36.559 --> 00:28:39.259 When criminals get access to that level of personal 00:28:39.259 --> 00:28:41.599 financial information, they can commit what's 00:28:41.599 --> 00:28:44.319 called synthetic identity fraud, creating entirely 00:28:44.319 --> 00:28:47.460 new financial profiles using parts of real identities, 00:28:47.660 --> 00:28:49.980 which is incredibly difficult to detect and clean 00:28:49.980 --> 00:28:51.880 up. But the source material is clear that fraud 00:28:51.880 --> 00:28:53.920 and abuse aren't limited to external hackers. 00:28:54.420 --> 00:28:56.900 Abuse can also be committed on consumers by the 00:28:56.900 --> 00:28:59.200 credit reporting agencies themselves. This is 00:28:59.200 --> 00:29:02.269 a crucial element of oversight. The source details 00:29:02.269 --> 00:29:05.309 a specific incident in 2013 where the Consumer 00:29:05.309 --> 00:29:08.890 Financial Protection Bureau, the CFPB, fined 00:29:08.890 --> 00:29:12.690 Equifax and TransUnion a combined $23 .3 million 00:29:12.690 --> 00:29:15.369 for deceptive practices. Deceiving customers 00:29:15.369 --> 00:29:17.470 about the cost of their services, right? Precisely. 00:29:17.490 --> 00:29:19.769 They were marketing credit monitoring services 00:29:19.769 --> 00:29:22.970 with clear disclosures of a $1 trial, but then 00:29:22.970 --> 00:29:25.150 automatically enrolling customers in subscriptions 00:29:25.150 --> 00:29:28.210 that billed them at $200 per year, often without 00:29:28.210 --> 00:29:30.960 clear conspicuous consent. So the CFTB stepped 00:29:30.960 --> 00:29:33.099 in. They did. They ordered the companies to pay 00:29:33.099 --> 00:29:36.480 that fine and refund millions to consumers, underscoring 00:29:36.480 --> 00:29:38.859 the necessity of strict federal oversight in 00:29:38.859 --> 00:29:41.440 this highly concentrated industry. And if we 00:29:41.440 --> 00:29:43.599 connect this to the bigger picture, the reason 00:29:43.599 --> 00:29:45.660 these fines and penalties exist is because of 00:29:45.660 --> 00:29:47.640 the foundational legislation designed to keep 00:29:47.640 --> 00:29:50.180 the system honest. This just underscores why 00:29:50.180 --> 00:29:52.119 laws like the Fair Credit Reporting Act, the 00:29:52.119 --> 00:29:55.380 FCRA, are so necessary. The FCRA doesn't just 00:29:55.380 --> 00:29:57.579 govern the three major credit reporting agencies, 00:29:57.859 --> 00:30:01.119 Experian, Equifax and TransUnion. It also covers 00:30:01.119 --> 00:30:03.380 a wide and often invisible network of specialty 00:30:03.380 --> 00:30:06.079 credit reporting agencies. These specialty agencies 00:30:06.079 --> 00:30:08.680 are everywhere. They really are. They cater to 00:30:08.680 --> 00:30:11.609 niche clients tracking specialized data. There 00:30:11.609 --> 00:30:13.589 are agencies dedicated to screening potential 00:30:13.589 --> 00:30:16.390 tenants for landlords, agencies tracking check 00:30:16.390 --> 00:30:18.950 writing history for retailers, agencies used 00:30:18.950 --> 00:30:21.650 by payday lenders, and even agencies used by 00:30:21.650 --> 00:30:24.589 casinos to track markers and debts. Wow. The 00:30:24.589 --> 00:30:26.710 scope of agencies collecting data on your financial 00:30:26.710 --> 00:30:29.089 habits is far wider than most of us realize, 00:30:29.329 --> 00:30:32.009 creating a parallel, less regulated financial 00:30:32.009 --> 00:30:35.450 profile. It truly is a vast, complex ecosystem, 00:30:35.509 --> 00:30:38.170 and that oversight is absolutely critical to 00:30:38.170 --> 00:30:40.599 ensuring fair treatment. So let's wrap up this 00:30:40.599 --> 00:30:43.059 extensive deep dive into your financial fingerprint. 00:30:43.759 --> 00:30:45.960 We took the opaque nature of the credit score 00:30:45.960 --> 00:30:48.559 and really broke it down into usable, weighted, 00:30:48.680 --> 00:30:51.900 and actionable knowledge points. The first inescapable 00:30:51.900 --> 00:30:53.799 key takeaway is that the lender looks for two 00:30:53.799 --> 00:30:56.980 things, ability, which is your income, and willingness, 00:30:57.099 --> 00:30:59.200 which is your score. And that willingness factor, 00:30:59.359 --> 00:31:02.039 your score, is overwhelmingly determined by just 00:31:02.039 --> 00:31:05.259 two primary pillars making up that crucial 65%. 00:31:05.259 --> 00:31:08.339 That's the 65 % rule that should guide your financial 00:31:08.339 --> 00:31:11.339 strategy. Timely payments are the single most 00:31:11.339 --> 00:31:14.440 important factor at 35%. And keeping your credit 00:31:14.440 --> 00:31:18.079 utilization ratio low, ideally under 10 % for 00:31:18.079 --> 00:31:21.160 maximum scoring, but certainly under 30%, is 00:31:21.160 --> 00:31:24.660 the second most important at 30%. If you aggressively 00:31:24.660 --> 00:31:27.480 manage those two metrics, you manage the vast 00:31:27.480 --> 00:31:30.240 majority of your financial destiny. And remember, 00:31:30.380 --> 00:31:32.299 the consumer protection built into the system. 00:31:32.589 --> 00:31:34.750 One of the core requirements of the Fair Credit 00:31:34.750 --> 00:31:37.289 Reporting Act in the U .S. is that credit reporting 00:31:37.289 --> 00:31:40.849 agencies must provide you with a free copy of 00:31:40.849 --> 00:31:43.309 your credit report once per year upon request. 00:31:44.049 --> 00:31:45.970 Utilizing that right is the only way to catch 00:31:45.970 --> 00:31:47.829 potential errors, those milli -data points we 00:31:47.829 --> 00:31:49.710 talked about, before they cost you favorable 00:31:49.710 --> 00:31:52.450 terms on a major loan. It's clear that this score 00:31:52.450 --> 00:31:54.549 is more than just a financial tool. It's become 00:31:54.549 --> 00:31:57.490 a societal gatekeeper with really heavy non -financial 00:31:57.490 --> 00:32:00.029 implications. Which raises an important question 00:32:00.029 --> 00:32:02.450 that extends beyond mere transactional finance. 00:32:03.730 --> 00:32:06.349 If the credit history and score are expertly 00:32:06.349 --> 00:32:08.609 designed to predict risk and reduce cost for 00:32:08.609 --> 00:32:10.930 the majority of lenders, and that's a valuable 00:32:10.930 --> 00:32:13.690 function for market efficiency, what are the 00:32:13.690 --> 00:32:16.690 ethical implications when this same score dictates 00:32:16.690 --> 00:32:18.769 whether a person can secure a job or housing? 00:32:19.759 --> 00:32:21.519 By using the score for employment screening, 00:32:21.700 --> 00:32:24.720 for example, the system unintentionally reinforces 00:32:24.720 --> 00:32:26.619 financial instability for those who are already 00:32:26.619 --> 00:32:28.960 struggling. It makes it harder for them to escape 00:32:28.960 --> 00:32:31.339 the very adverse credit history the system is 00:32:31.339 --> 00:32:33.740 built to punish. So while the scoring system 00:32:33.740 --> 00:32:35.900 standardizes risk assessment without prejudice 00:32:35.900 --> 00:32:38.319 to an applicant's background, the real -world 00:32:38.319 --> 00:32:40.480 consequences are anything but unbiased, and they 00:32:40.480 --> 00:32:43.130 can often magnify existing inequality. So the 00:32:43.130 --> 00:32:45.390 final thought we'll leave you with is this. Consider 00:32:45.390 --> 00:32:47.769 the implication of those specialty credit reporting 00:32:47.769 --> 00:32:50.890 agencies, the ones catering specifically to landlords, 00:32:51.170 --> 00:32:53.730 payday lenders, and employers. How might their 00:32:53.730 --> 00:32:56.710 collection of granular niche data be compiling 00:32:56.710 --> 00:32:59.349 an even more fine -tuned and perhaps more restrictive 00:32:59.349 --> 00:33:02.109 financial profile of you that the big three FICO 00:33:02.109 --> 00:33:04.569 bureaus don't even capture? And how could that 00:33:04.569 --> 00:33:06.470 be influencing decisions about where you live 00:33:06.470 --> 00:33:07.710 or where you work right now?